| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| language: |
| - en |
| tags: |
| - geografy |
| - wildfire |
| - nature |
| - preservation |
| pretty_name: IGNIS - Intelligent Geospatial Network for Incendiary Surveillance |
| size_categories: |
| - n<1K |
| --- |
| |
| # IGNIS - Intelligent Geospatial Network for Incendiary Surveillance |
|
|
| A dataset for **image segmentation of wildfires** in satellite/aerial imagery. The dataset contains **paired images and labels**, where each label highlights wildfire-affected regions. |
|
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|  |
|
|
| ## Dataset Summary |
|
|
| This dataset was created to support research in **wildfire detection, monitoring, and environmental risk assessment**. It can be used for training and evaluating segmentation models. |
|
|
| * **Task:** Image Segmentation |
| * **Domain:** Remote sensing / Environmental monitoring |
| * **License:** CC BY 4.0 |
|
|
| ## Supported Tasks |
|
|
| * **Image Segmentation** – Identify wildfire regions pixel-by-pixel. |
| * **Potential Applications:** |
|
|
| * Early wildfire detection |
| * Environmental monitoring |
| * Risk modeling and prevention systems |
|
|
| ## Dataset Structure |
|
|
| ### Data Splits |
|
|
| The dataset is divided into: |
|
|
| * `train` |
| * `validation` |
| * `test` |
|
|
| ## Data Fields |
|
|
| * **image** (`Image`) – RGB image |
| * **label** (`Label`) – TXT file containing coordinates for the polygons following YOLOv11 format |
|
|
| Example: |
|
|
| ``` |
| { |
| "image": "train/images/image_001.jpg", |
| "label": "train/labels/image_001.txt" |
| } |
| ``` |
|
|
| ## Dataset Creation |
|
|
| ### Motivation |
|
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| Wildfires are an increasing threat worldwide. This dataset was built to help researchers and engineers develop segmentation models that can detect wildfire-affected areas in aerial/satellite imagery. |
|
|
| This dataset is originally a personal project, but anyone with expert knowledge in meteorological, geographic, geophisical and related areas might feel free to reach out and help expand the dataset and increase its quality. |
|
|
| ### Source Data |
|
|
| * **Collection Process:** Images were sourced from open satellite/aerial datasets. |
| * **Annotation Process:** Masks were generated using a mix between manual labelling and automatic polygon generation thanks to Roboflow's tools. |
|
|
| ### Annotations |
|
|
| * **Annotation Guidelines:** Each class is labeled as: |
|
|
| * 0 → Burned Ground (burnt) |
| * 1 → Smoke Cloud (smoke_cloud) |
| * 2 → Smoke Column (smoke_column) |
| * 3 → Wildfire (wildfire) |
|
|
| ## Licensing Information |
|
|
| * **Dataset License:** CC BY 4.0 |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ``` |
| @dataset{ignis, |
| title = {Intelligent Geospatial Network for Incendiary Surveillance}, |
| author = {Matheus J. G. Silva}, |
| year = {2025}, |
| url = {https://huggingface.co/datasets/matjs/ignis} |
| } |
| ``` |
|
|
| ## Acknowledgements |
|
|
| * [NASA FIRMS - Fire Information for Resource Management System](https://firms.modaps.eosdis.nasa.gov/) |
| * [NASA Earth Observatory](https://earthobservatory.nasa.gov) |
| * Inspired by the growing need for **AI-assisted wildfire monitoring**. |
